Releasesby GlyphTransformers Release v5.16.1
Release v5.16.1 This is a special release as we include GLM! (and a few small fixes) GLM-5.3-Flash GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series.
Also shipped Aug 26, 2026Hugging Face in August 2026 →
You can now view real-time logs for your Python UDFs in Cloud Logging
You can now view real-time logs for your Python UDFs in Cloud Logging. This feature is generally available .
Cortex Extension references in agent skills (General availability)
Referencing a Cortex Extension from an agent skill is now generally available. Set the skill source type to CORTEX_EXTENSION and path to the extension's fully qualified name, and Snowflake expands the reference into the extension's member skills at request time, so you don't list each skill individually.
Cortex Agents Coding Agent (General availability)
The Cortex Agents Coding Agent is now generally available. Add the code_toolset_all tool type to a Cortex Agent, and Snowflake provisions and manages a sandbox backed by the same runtime that powers Snowflake CoCo: bash, file read/write/edit, grep, glob, web search, SQL execution, and skills.
Organization user types (General availability)
With this release, you can specify whether an organization user represents a person or a service. Set the TYPE property to SERVICE for a service or application that interacts with Snowflake without human interaction. If you don't specify a type, the organization user is a PERSON user, which matches the previous behavior.
Semantic Studio (Preview)
Semantic Studio is now available in public preview for all accounts. Semantic Studio is the authoring environment for semantic views in Workspaces, combining conversational authoring with CoCo, direct YAML editing, and deployment to the live Snowflake object in a single surface.
Zerobus Ingest into streaming tables is now generally available
Ingesting into streaming tables with Zerobus Ingest in Lakeflow Connect is now generally available. Writing to a streaming table works the same way as writing to a managed Delta table, with the same limits and quotas. See Create or identify the target table .
More from TransformersEvery Transformers release →
Transformers Release: v5.16.0
Release v5.16.0 New Model additions Qwen4-Exp Qwen4-Exp builds on Qwen3.5's hybrid text and multimodal architecture with three key components: GatedResidual (GR), Qwen Sparse Attention (QSA), and Per-Layer Embedding (PLE). GR is a Qwen-developed residual architecture that combines Hyper-Connection with GatedNorm.
Transformers Patch release: v5.15.1
Patch release v5.15.1 This patch most notably solves a few issues with DFlash and MTP candidate generators, as well as an issue where images could sometimes not be processed on accelerator if using Lanczos filter.
Transformers Release 5.17.0
Release v5.17.0 New Model additions HYV4 Hy4-Preview is a 780B-parameter mixture-of-experts language model that activates 49B parameters per token. Each MoE layer holds 256 routed experts plus one always-active shared expert and routes every token to 8 of them. The context window is 1M tokens.
Transformers Release: v5.15.0
Release v5.15.0 New Model additions Meta Muse Glimmer Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it can be deployed to local setups for privacy-aware applications such as coding, document analysis, personal...
Transformers Release 5.18.0
New Model additions Nemotron 3 Diarization Nemotron 3 Diarization is an open-weight streaming speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference, handles up to eight speakers, and orders speaker outputs by each speaker's first arrival in the input audio.
Transformers Release v5.19.0
Release v5.19.0 New Model additions EmbeddingGemma2 EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture. It encodes text, images, audio, and video, individually or combined in one input, into a shared 768-dimensional vector space for cross-modal retrieval, semantic similarity, clustering, and classification.
Transformers Patch release: v5.14.1
Patch release v5.14.1 This patch solves a few issues which appeared when integrating Inkling model, most notably an issue affecting models using EncoderDecoderCache during assisted generation. It also fixes an issue that could appear during prefill with StaticCache and sdpa without padding for Inkling which uses a position_bias.
Transformers Release v5.14.0
Release v5.14.0 New Model additions Inkling (fresh from Thinking Machines): 975B total, 41B active Add Inkling model #47347 by @molbap @Cyrilvallez @eustlb and @zucchini-nlp Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs.